Unmanned aerial vehicle low-altitude logistics distribution path scheduling method, device, equipment and medium

Abstract

The invention relates to an unmanned aerial vehicle low-altitude logistics distribution path scheduling method and device, equipment and a medium. The method comprises the following steps: acquiring real-time obstacle distribution data in a distribution area, and obtaining accurate position information of obstacle distribution; according to the accurate position information, performing preliminary calculation on the flight path of the unmanned aerial vehicle to obtain a preliminary path planning result; acquiring wind speed information, resident distribution data and noise sensitive area information; if the wind speed of the current area exceeds a preset threshold value, the paths with the safety scores lower than the preset threshold value in the preliminary path planning result are removed, and adjusted path segment data are obtained; and according to the adjusted path segment data, updating a corresponding path segment in the preliminary path planning result to obtain adjusted path planning data. By adopting the method, the obstacle and wind speed updating information can be obtained in real time, the flight path of the unmanned aerial vehicle is dynamically adjusted, and the distribution efficiency and safety are improved.

Description

Technical Field

[0001] The present invention belongs to the field of drone logistics distribution, and in particular relates to a method, device, equipment and medium for scheduling drone low-altitude logistics distribution paths. Background Art

[0002] As a key innovation in modern logistics, drone-based low-altitude delivery plays a significant role in improving urban distribution efficiency and alleviating traffic pressure. This technology not only enables fast and accurate cargo transportation but also offers unmatched flexibility in complex environments, making it an essential component of future smart city development.

[0003] In traditional technologies, many methods rely on static data of the delivery area for path planning and direction selection. However, obstacles such as buildings and trees in urban environments are complexly distributed and dynamically changing. The flight path planning of drones must respond to these unpredictable factors in real time.

[0004] This need for dynamic adjustments further increases the requirements for flight direction selection, as direction not only affects energy consumption and flight stability but also involves balancing noise control with impacts on residents' lives. The complexity of direction selection is directly linked to the ability to integrate real-time meteorological data. For example, changes in wind direction and speed can significantly interfere with flight stability. Failure to adjust direction in a timely manner can lead to delivery failure or even safety hazards. Summary of the Invention

[0005] Based on this, it is necessary to provide methods, devices, equipment and media for scheduling low-altitude logistics distribution paths of drones to address the above technical problems.

[0006] In the first aspect, this application provides a method for dispatching low-altitude logistics distribution paths using drones, including:

[0007] Obtain real-time obstacle distribution data within the delivery area and obtain accurate location information of obstacle distribution;

[0008] Based on the precise location information, the flight path of the UAV is preliminarily calculated to obtain the preliminary path planning result;

[0009] Obtain path influencing parameters from the preliminary path planning results; path influencing parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information;

[0010] Based on the resident distribution data and noise sensitive area information, the resident distribution area and noise sensitive area are obtained;

[0011] Determine whether the natural wind speed in the target area in the preliminary path planning results exceeds the wind speed thresholds of residential areas and noise-sensitive areas, and obtain a judgment result;

[0012] If the wind speed in the target area exceeds the wind speed threshold for residential areas and noise-sensitive areas, the paths with safety scores lower than the preset threshold are eliminated from the preliminary path planning results to obtain adjusted path segment data.

[0013] According to the adjusted path segment data, the corresponding path segment in the preliminary path planning result is updated to obtain the adjusted path planning data.

[0014] Furthermore, obtaining the obstacle distribution data on the delivery path to obtain accurate location information of the obstacle distribution includes:

[0015] The original information of building locations and dynamic objects is obtained by scanning to obtain a preliminary obstacle distribution dataset;

[0016] Based on the preliminary obstacle distribution dataset, a three-dimensional model is constructed to spatially reconstruct the obstacle distribution and obtain three-dimensional structural data containing accurate change information;

[0017] Comparing the position change of the dynamic object in the three-dimensional structure data with a preset threshold value to obtain a comparison result;

[0018] If the comparison result shows that the position change of the dynamic object exceeds the preset threshold, the dynamic object is marked as a high-priority monitoring object and the real-time movement trajectory of the dynamic object is obtained;

[0019] Based on real-time moving trajectories, the dynamic changes of obstacle distribution are updated to obtain the latest obstacle distribution data;

[0020] Based on the latest obstacle distribution data, the position of the dynamic object at the preset time point is predicted to obtain the area where the dynamic object is located at the preset time point;

[0021] Adjust the scanning frequency according to the area range, increase the data collection density, and obtain accurate location information of obstacle distribution.

[0022] Furthermore, if the judgment result is that the wind speed in the target area exceeds the preset wind speed threshold, paths with safety scores lower than the preset threshold are eliminated from the preliminary path planning results to obtain adjusted path segment data, including:

[0023] If the wind speed in the target area exceeds the preset wind speed threshold, a risk marking instruction is generated. The risk marking instruction is used to mark the target area as a high-risk area and obtain a risk marking result;

[0024] Based on the risk marking results, the path segments passing through high-risk areas in the preliminary path planning results are obtained and analyzed using a pre-established path optimization model to obtain a set of path segments that need to be adjusted.

[0025] Based on the set of path segments that need to be adjusted, the wind direction information and wind speed information are integrated, and the path segments with safety scores lower than the preset threshold are eliminated to obtain the adjusted path segment data.

[0026] Furthermore, the method further comprises:

[0027] Based on the adjusted path planning data, the angle adjustment value of the flight direction is calculated by the vector analysis method to obtain the final flight direction adjustment value;

[0028] The final flight direction adjustment value and the adjusted path planning data are integrated through real-time data integration technology to obtain the real-time flight trajectory data of the UAV in a dynamic environment;

[0029] Obtain flight time and battery consumption based on real-time flight trajectory data of drones in dynamic environments;

[0030] Calculate the actual flight efficiency based on flight time and battery consumption to determine whether the delivery efficiency requirements are met and draw conclusions.

[0031] If the result is that the delivery efficiency requirement is not met, the flight distance is reduced according to the waypoint optimization formula to obtain the optimized flight path;

[0032] Based on the optimized flight path, the heading angle is adjusted using the ground speed maximization principle to obtain the final adjusted flight planning data.

[0033] Furthermore, the step of calculating the angle adjustment value of the flight direction based on the adjusted path planning data by a vector analysis method to obtain a final flight direction adjustment value includes:

[0034] Based on the adjusted path planning data, the vector analysis method is used to calculate the angle between the wind direction and the flight direction to obtain the optimized flight direction parameters;

[0035] Based on the optimized flight direction parameters, combined with the acquired resident distribution data and noise-sensitive area information, it is determined whether the optimized flight direction parameters cause the noise impact to exceed the preset noise threshold, and a judgment result is obtained;

[0036] If the judgment result is that the noise impact exceeds the preset noise threshold, the flight direction is changed, the flight altitude is increased, or the flight attitude is adjusted to obtain the final flight direction adjustment value.

[0037] Furthermore, the angle between the wind direction and the flight direction is calculated based on the adjusted path planning data using a vector analysis method to obtain optimized flight direction parameters, including:

[0038] Obtain the flight direction angle and wind direction angle of the flight direction from the adjusted path planning data;

[0039] Based on the flight direction angle and wind direction angle of the flight direction, the vector analysis method is used to calculate the angle between the wind direction and the current flight direction to obtain the preliminary direction adjustment angle:

[0040] α=|(φ-θ)|

[0041]

[0042] Where θ is the flight direction angle, φ is the wind direction angle, α is the angle between the wind direction and the current flight direction, β is the initial direction adjustment angle, W is the wind speed, and V is the aircraft airspeed;

[0043] Based on the preliminary direction adjustment angle, the fusion energy consumption balance and control conditions are compared with the preset threshold range to obtain the comparison results;

[0044] If the comparison result shows that the direction adjustment angle exceeds the threshold range, a restriction processing mechanism is adopted for the angle, and the angle is clipped through the data screening tool to obtain the adjustment angle range that meets the conditions;

[0045] Based on the adjustment angle range that meets the conditions and combined with the real-time changes of wind direction data and wind speed data, the dynamic impact on the flight direction parameters is analyzed to obtain the optimized flight direction parameters.

[0046] Furthermore, the real-time data integration technology is used to integrate the final flight direction adjustment value and the adjusted path planning data to obtain real-time flight trajectory data of the UAV in a dynamic environment, including:

[0047] Based on the final flight direction adjustment value and the adjusted path planning data, a unified data structure is generated through real-time data integration technology to obtain a standardized flight control data set;

[0048] Generate flight instructions based on a standardized flight control data set; flight instructions are used to instruct the UAV to fly according to the flight control data set and provide feedback on real-time flight trajectory data in a dynamic environment;

[0049] Based on real-time flight trajectory data, the onboard sensors continuously monitor the changes in obstacle distribution and updated information on wind direction and speed to obtain monitoring results;

[0050] If changes in obstacle distribution are detected or wind speed exceeds the preset safety threshold, an adjustment instruction is generated; the adjustment instruction is used to activate the dynamic path adjustment mechanism, adjust the flight trajectory in real time, and obtain updated flight trajectory data of the UAV in complex environments.

[0051] Secondly, this application also provides a UAV low-altitude logistics distribution path scheduling device, including:

[0052] The data acquisition module is used to obtain real-time obstacle distribution data within the delivery area and obtain accurate location information of the obstacle distribution;

[0053] The path planning module is used to perform preliminary calculations on the UAV flight path based on precise location information and obtain preliminary path planning results;

[0054] The parameter acquisition module is used to obtain the path-affecting parameters in the preliminary path planning results; the path-affecting parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information;

[0055] The regional distribution acquisition module is used to obtain the resident distribution area and noise-sensitive area based on the resident distribution data and noise-sensitive area information;

[0056] A judgment module is used to judge whether the natural wind speed in the target area in the preliminary path planning result exceeds the preset wind speed threshold of the residential distribution area and the noise-sensitive area, and obtain a judgment result;

[0057] A data updating module is configured to, if the wind speed in the target area exceeds a preset wind speed threshold, eliminate paths with safety scores lower than the preset threshold from the preliminary path planning results, thereby obtaining adjusted path segment data;

[0058] The path updating module is used to update the corresponding path segment in the preliminary path planning result according to the adjusted path segment data to obtain the adjusted path planning data.

[0059] In a third aspect, the present application also provides a computer device comprising a memory and a processor, wherein the memory stores at least one instruction, at least one program, code or instruction set, and the at least one instruction, at least one program, code or instruction set is loaded and executed by the processor to implement the drone low-altitude logistics distribution path scheduling method described in any of the embodiments of the present application.

[0060] In a fourth aspect, the present application also provides a computer-readable storage medium, in which at least one program code is stored, and the program code is loaded and executed by a processor to implement the drone low-altitude logistics distribution path scheduling method described in any of the embodiments of the present application.

[0061] The aforementioned method, device, equipment, and medium for scheduling low-altitude logistics delivery routes for drones utilize multi-sensor data fusion to acquire real-time environmental information. This triggers path replanning when wind speed exceeds a threshold or new obstacles are detected. This system takes into account the complex and dynamic distribution of obstacles such as buildings and trees in urban environments, allowing for timely flight adjustments. This system avoids noise-sensitive areas and disrupts residents' lives, enabling adaptive path planning for drones in complex and dynamic environments, improving flight safety and mission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0063] Figure 1 Schematic diagram of a flow chart of a method for dispatching a low-altitude logistics distribution path of a drone in one embodiment;

[0064] Figure 2 A schematic diagram of a process flow for obtaining obstacle distribution data on a delivery path in one embodiment;

[0065] Figure 3 Schematic diagram of the structure of a UAV low-altitude logistics distribution path scheduling device in one embodiment. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0067] In one embodiment, Figure 1 As shown, a method for scheduling low-altitude logistics delivery routes using drones is provided. This embodiment uses the method applied to a terminal as an example. It is understood that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0068] Step S101: Acquire real-time obstacle distribution data within the delivery area to obtain accurate location information of the obstacle distribution.

[0069] For example, obstacle distribution data is collected in real time from complex urban environments through a sensor network, the locations of buildings and dynamic objects are scanned, and three-dimensional modeling technology is used to generate real-time map data of obstacle distribution to obtain accurate location information of obstacle distribution.

[0070] Step S102: Preliminary calculation of the UAV flight path is performed based on the precise location information to obtain a preliminary path planning result.

[0071] For example, a path planning algorithm can be used to perform a preliminary calculation of the drone's flight path based on precise location information, yielding a preliminary path planning result. A path planning algorithm uses mathematical models and logical rules to calculate a feasible path from a starting point to a destination within a given environment. Mainstream path planning algorithms include genetic algorithms and the A-star algorithm.

[0072] For example, the A-star algorithm can be used to perform a preliminary calculation of the drone's flight path to obtain a preliminary path planning result. The A-star algorithm is a heuristic graph search algorithm that searches for the optimal path within the obstacle distribution graph structure provided by the path planning model by combining the distance from the starting point to the current node and the estimated distance from the current node to the end point.

[0073] Step S103: Obtain path influencing parameters from the preliminary path planning result; the path influencing parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information.

[0074] Among them, wind direction and speed information can be obtained through meteorological stations and distributed sensor networks in the logistics distribution area, and resident distribution data and noise-sensitive area information can be obtained from the city database.

[0075] For example, wind speed information from the preliminary route planning results is collected through a weather station within the logistics distribution area. The weather station is equipped with high-precision meteorological measurement instruments such as anemometers and hygrometers, which can collect wind direction and speed data in real time. Resident distribution data and information on noise-sensitive areas are obtained from the city database.

[0076] Step S104: obtaining the resident distribution area and the noise sensitive area based on the resident distribution data and the noise sensitive area information.

[0077] Noise-sensitive areas are those with low noise tolerance and requiring special protection. These areas include hospitals, schools, and nursing homes. Noise tolerance thresholds are set based on regional regulations using a graded wind speed threshold model, such as 6 m / s for residential areas, 5 m / s for hospitals, 7 m / s for schools, and 4 m / s for nursing homes.

[0078] For example, the acquired residential distribution data and noise-sensitive area information are unified into a unified coordinate system, and spatial clustering tools are used to aggregate discrete residential point data into continuous residential distribution areas. Spatial clustering tools are algorithms or tools used in geographic information systems (GIS) and spatial data analysis to identify dense or similar areas in spatial data. Noise-sensitive area types, such as hospitals, schools, and nursing homes, are screened and supplemented with attribute information, such as sensitivity levels and allowable wind speed thresholds, to determine the noise-sensitive areas.

[0079] Step S105 , determining whether the natural wind speed in the target area in the preliminary path planning result exceeds a preset wind speed threshold value for the residential area and the noise-sensitive area, and obtaining a determination result.

[0080] For example, the natural wind speed of the residential area and the noise-sensitive area obtained in real time from the weather station is compared with the corresponding preset wind speed threshold to determine whether the wind speed in the current area exceeds the preset wind speed threshold and obtain a judgment result.

[0081] In step S106 , if the judgment result is that the wind speed in the target area exceeds the preset wind speed threshold, the paths with safety scores lower than the preset threshold are eliminated from the preliminary path planning results to obtain adjusted path segment data.

[0082] For example, there are multiple candidate path segments in the preliminary path planning results. A genetic algorithm is used to calculate the safety scores of the multiple candidate path segments, and candidate path segments with safety scores lower than a preset threshold are eliminated to obtain adjusted path segment data.

[0083] Step S107 : updating the corresponding path segment in the preliminary path planning result according to the adjusted path segment data to obtain the adjusted path planning data.

[0084] Exemplarily, after eliminating candidate path segments with safety scores lower than a preset threshold, the corresponding path segments in the preliminary path planning results are replaced with path segments with safety scores higher than the preset threshold to obtain adjusted path planning data.

[0085] The above-mentioned drone low-altitude logistics distribution path scheduling method adopts a path segment-level safety optimization mechanism, which can achieve precise obstacle avoidance in local high-risk areas while maintaining the global path skeleton, thereby improving the drone's wind resistance and obstacle avoidance efficiency.

[0086] In one embodiment, Figure 2 As shown, the method of obtaining the obstacle distribution data on the delivery path and obtaining the accurate location information of the obstacle distribution includes:

[0087] In step S201 , original information of building locations and dynamic objects is acquired by scanning to obtain a preliminary obstacle distribution data set.

[0088] Among them, the scanning methods include lidar, visual sensors and inertial measurement units.

[0089] For example, a lidar with a frequency of 80 Hz, 120 Hz, or 160 Hz can be selected to perform high-frequency scanning in the logistics distribution area, which can quickly obtain the original information of the location of buildings and dynamic objects and obtain a preliminary obstacle distribution data set.

[0090] Step S202 : Based on the preliminary obstacle distribution data set, a three-dimensional model is constructed to spatially reconstruct the obstacle distribution, thereby obtaining three-dimensional structure data containing accurate change information.

[0091] A 3D model is a digital representation of real-world objects or scenes, using mathematical methods and computer technology to represent them in three dimensions: length, width, and height. It accurately or abstractly describes an object's shape, structure, and spatial relationships through geometric elements such as points, lines, surfaces, and volumes, as well as attributes such as color, texture, and material.

[0092] For example, after extracting features from the preliminary obstacle distribution dataset, multiple frames of high-frequency scanning data are timestamped and synchronized to ensure the position consistency of dynamic objects in consecutive frames. The building point cloud data is divided into different areas, and geometric surfaces such as planes and cylinders are fitted to each area to generate a parametric model to intuitively present the three-dimensional structure of the building. Based on the multi-target tracking algorithm, the dynamic object point data are associated in consecutive frames to generate motion trajectories. For dynamic objects, a templated three-dimensional model is established, and the coordinates of the model in three-dimensional space are updated according to the real-time position and posture to obtain three-dimensional structure data containing precise change information.

[0093] Step S203 : comparing the position change of the dynamic object in the three-dimensional structure data with a preset threshold value to obtain a comparison result.

[0094] For example, the three-dimensional coordinates and timestamps of the dynamic object are parsed from the three-dimensional structure data, and the velocity is calculated by difference:

[0095]

[0096] Among them, v is the velocity of the dynamic object, (x t ,y t ,z t ) is the three-dimensional coordinate of the dynamic object at time t, (x t-1 ,y t-1 ,z t-1 ) is the three-dimensional coordinate of the dynamic object at time t-1, t t is the timestamp of the current moment, t t-1 The timestamp of the previous moment is used to generate the obstacle displacement field through timestamp difference calculation. The position change gradient within the 0.1s interval is recorded, and the threshold is dynamically set according to the object type. For example, the vehicle displacement threshold is set to 0.6m / s for the urban road speed limit; the pedestrian displacement threshold is set to 1.5m / s to represent the sudden running speed.

[0097] In step S204 , if the comparison result shows that the position change of the dynamic object exceeds a preset threshold, the dynamic object is marked as a high-priority monitoring object, and the real-time movement trajectory of the dynamic object is obtained.

[0098] Among them, high-priority monitoring objects refer to dynamic objects whose position changes exceed a threshold and are marked as high-risk objects, which need to be monitored first.

[0099] For example, after calculating that the displacement change of the dynamic object exceeds a threshold, the dynamic object is marked as a high-level monitoring object, and the real-time movement trajectory of the dynamic object in the next 0.5 seconds is obtained.

[0100] Step S205: Based on the real-time movement trajectory, the dynamic changes of obstacle distribution are updated to obtain the latest obstacle distribution data.

[0101] Exemplarily, the preliminary obstacle distribution data set is updated according to the acquired real-time moving trajectory of the dynamic object to obtain the obstacle distribution data after the dynamic object moves.

[0102] Step S206 : predicting the position of the dynamic object at a preset time point based on the latest obstacle distribution data, and obtaining the area where the dynamic object is located at the preset time point.

[0103] Among them, the preset time point refers to the time when the drone reaches a specified area on the predicted moving trajectory of the dynamic object, and the position prediction of the object is completed through the long short-term memory network (LSTM).

[0104] For example, based on the obstacle distribution data after the dynamic object moves, the trajectory envelope is predicted through the LSTM neural network, and the position of the dynamic object at a specified time point is predicted to obtain the area where the dynamic object is located when it reaches the specified time point.

[0105] Step S207: Adjust the scanning frequency according to the area range to increase the data acquisition density and obtain accurate location information of the obstacle distribution.

[0106] For example, the scanning frequency is adjusted according to the range of the dynamic object at the preset time point and the coverage radius of the location area reached by the drone at the preset time point. For example, the scanning frequency is 50Hz within a coverage radius of 50 meters, and the collection density is increased within a radius of 10 meters, and a scanning frequency of 80Hz is selected to obtain accurate location information of the obstacle distribution.

[0107] In this embodiment, a sensor network is constructed by cooperating with multiple sensors, breaking through the physical limitations of a single sensor, and predicting the real-time trajectory of dynamic objects to obtain accurate location information of obstacle distribution, thereby improving the success rate of obstacle avoidance.

[0108] In one embodiment, if the judgment result is that the wind speed in the target area exceeds the preset wind speed threshold, paths with safety scores lower than the preset threshold are eliminated from the preliminary path planning results to obtain adjusted path segment data, including:

[0109] Step S301: If the wind speed in the target area exceeds the preset wind speed threshold, a risk marking instruction is generated. The risk marking instruction is used to mark the current area as a high-risk area and obtain a risk marking result.

[0110] Exemplarily, a graded wind speed threshold model is used to determine whether the wind speed in the current area exceeds a preset wind speed threshold. After detecting that it exceeds the threshold, the corresponding area is marked as a high-risk area in the preliminary path data, and a risk marking result is obtained, waiting for adjustment.

[0111] In step S302, based on the risk marking results, the path segments passing through the high-risk areas in the preliminary path planning results are obtained, and a pre-established path optimization model is used to analyze them to obtain a set of path segments that need to be adjusted.

[0112] Among them, the path optimization model is an optimization method for discretized path search through dynamic programming and mixed integer programming.

[0113] Exemplarily, path segments passing through high-risk areas are obtained from the preliminary path planning results. By adopting a pre-established path optimization model for discretized path search through dynamic programming and mixed integer programming, the obtained high-risk path segments are analyzed according to the constraints to obtain a set of path segments that need to be adjusted.

[0114] In step S303, based on the set of path segments that need to be adjusted, the wind direction information and wind speed information are integrated, and the path segments with safety scores lower than a preset threshold are eliminated to obtain adjusted path segment data.

[0115] For example, the wind speed vector Decompose the tangential component W t (parallel path) and the normal component W n (Vertical path) Calculate wind disturbance torque:

[0116]

[0117] Where ρ is the air density, C d is the drag coefficient, A is the windward projected area (m 2 ), W n is the normal component of wind speed (m / s), d is the length of the lever arm (m). The safety score of the path segment is calculated based on the wind disturbance torque:

[0118]

[0119] Among them, S safe For safety score, e -k1·Mdis an exponential decay model, k1 is the wind torque risk probability mapping value, which is fitted by the failure database. Path segments with safety scores below the preset threshold are eliminated, and the path segments are adjusted to obtain the adjusted path segment data.

[0120] In this embodiment, by marking high-risk wind speed areas, analyzing the safety score of each path segment, and eliminating paths below a preset threshold, the safety and efficiency of drone logistics delivery are improved.

[0121] In one embodiment, the method further comprises:

[0122] Step S401: Based on the adjusted path planning data, the angle adjustment value of the flight direction is calculated by a vector analysis method to obtain the final flight direction adjustment value.

[0123] Among them, vector analysis method is a tool in mathematics and physics based on vectors and their operations to study spatial geometry, physical field distribution and dynamic changes.

[0124] For example, the adjusted path planning data is subjected to a vector analysis method to calculate the angle of the UAV's flight direction, and the flight angle is adjusted to obtain an adjusted flight direction adjustment value.

[0125] Step S402: The final flight direction adjustment value and the adjusted path planning data are integrated through real-time data integration technology to obtain real-time flight trajectory data of the UAV in a dynamic environment.

[0126] Among them, real-time data integration technology refers to a technical system that seamlessly integrates multi-source heterogeneous data in a short period of time, usually in milliseconds to seconds, through efficient data collection, transmission, processing and fusion mechanisms to form a unified and accurate real-time data set to support dynamic decision-making.

[0127] For example, in a UAV flight scenario in a dynamic environment, real-time data integration technology is used to fuse the flight direction adjustment value with the adjusted path planning data to generate accurate real-time flight trajectory data.

[0128] Step S403: Obtain flight time and battery consumption based on the real-time flight trajectory data of the UAV in a dynamic environment.

[0129] For example, in the real-time flight trajectory data of the drone, each trajectory point contains a precise timestamp. By calculating the difference between the starting point and the end point timestamp, the segmented flight time or the full flight time can be obtained. The battery charge and discharge current (A) is measured in real time through current sensors, such as Hall effect sensors and shunts, and the accumulated charge (Ah) is calculated by time integration to estimate the battery consumption.

[0130] Step S404: Calculate the actual flight efficiency based on the flight duration and battery consumption, determine whether the delivery efficiency requirements are met, and obtain a determination result.

[0131] For example, the actual flight efficiency is calculated using the calculated flight time and battery consumption using an efficiency formula based on power consumption:

[0132]

[0133] Compare the actual flight efficiency with the ideal efficiency calculated based on the ideal endurance data provided by the drone manufacturer to determine whether the actual flight efficiency can meet the delivery efficiency requirements.

[0134] In step S405, if the result of the judgment is that the delivery efficiency requirement is not met, the flight distance is reduced according to the waypoint optimization formula to obtain an optimized flight path.

[0135] Among them, waypoint optimization formulas in drone path planning are usually based on mathematical algorithms, such as the shortest path algorithm and heuristic algorithm, which improve efficiency by reducing the flight distance.

[0136] For example, when the actual flight efficiency does not meet the delivery efficiency requirements, the redundant waypoints are compressed through the waypoint optimization formula to reduce the flight distance and optimize the flight path.

[0137] Step S406: Based on the optimized flight path, the heading angle is adjusted using the ground speed maximization principle to obtain the final adjusted flight planning data.

[0138] Among them, the ground speed maximization principle means that in a dynamic environment with wind interference, the ground speed reaches the maximum value by adjusting the heading angle of the drone, thereby optimizing flight efficiency.

[0139] For example, by adjusting the angle between the wind direction and the target route of the UAV on the optimized flight path, the maximum ground speed is calculated, thereby improving flight efficiency.

[0140] In this embodiment, by constructing a dynamic optimization chain, a coordinated leap in distribution efficiency and economic benefits can be achieved while ensuring safety.

[0141] In one embodiment, the calculating the angle adjustment value of the flight direction based on the adjusted path planning data by a vector analysis method to obtain the final flight direction adjustment value includes:

[0142] Step S501: Based on the adjusted path planning data, a vector analysis method is used to calculate the angle between the wind direction and the flight direction to obtain optimized flight direction parameters.

[0143] For example, based on the adjusted path planning data, the UAV airspeed is obtained in real time through an airspeed meter, and the angle between the wind direction and the flight direction is calculated using an airspeed vector synthesis model to obtain optimized flight direction parameters.

[0144] Step S502: Based on the optimized flight direction parameters, combined with the acquired resident distribution data and noise-sensitive area information, it is determined whether the optimized flight direction parameters cause the noise impact to exceed a preset noise threshold, and a determination result is obtained.

[0145] For example, based on the optimized flight direction parameters, we can determine the aircraft's position and attitude along each path. Combined with acquired resident distribution data and information about noise-sensitive areas, we calculate the noise level generated by the aircraft at each point along the path. We then determine whether the noise level exceeds the preset noise threshold for the area.

[0146] In step S503, if the judgment result is that the noise impact exceeds the preset noise threshold, the flight direction is changed, the flight altitude is increased, or the flight attitude is adjusted to obtain a final flight direction adjustment value.

[0147] Among them, changing the flight direction can be achieved by deflecting the heading angle, so that the main lobe of the blade noise will deviate from the sensitive area to reduce noise; increasing the flight altitude is because the noise decays inversely proportional to the square of the sound intensity and distance; adjusting the flight attitude is because when the pitch angle is controlled at 5°~8°, the blade vortex separation point can be moved backward, reducing the vortex shedding noise, which is the main source of broadband noise.

[0148] For example, when the noise of drone logistics delivery exceeds a preset threshold according to the calculated optimized flight parameters, the most appropriate flight direction adjustment value can be obtained by deflecting the heading angle, increasing the flight altitude and adjusting the flight attitude.

[0149] In this embodiment, the noise impact is reduced by optimizing the flight direction.

[0150] In one embodiment, the step of calculating the angle between the wind direction and the flight direction based on the adjusted path planning data using a vector analysis method to obtain optimized flight direction parameters includes:

[0151] Step S601: Obtain the flight direction angle and wind direction angle of the flight direction from the adjusted path planning data.

[0152] Among them, the flight direction angle can be directly obtained through the navigation system, and the wind direction angle can be obtained through ground weather stations, weather radars or satellites. It is usually expressed as "wind direction XX degrees", such as east wind is 90 degrees).

[0153] For example, the real-time position data of the aircraft is obtained by a GNSS receiver, and the track direction is calculated by combining the position changes before and after, thereby obtaining the flight direction angle. The wind direction angle is obtained by a ground weather station.

[0154] Step S602: Based on the flight direction angle and wind direction angle of the flight direction, a vector analysis method is used to calculate the angle between the wind direction and the current flight direction to obtain a preliminary direction adjustment angle:

[0155] α=|(φ-θ)|

[0156]

[0157] Where θ is the flight direction angle, φ is the wind direction angle, α is the angle between the wind direction and the current flight direction, β is the initial direction adjustment angle, W is the wind speed, and V is the aircraft airspeed.

[0158] In step S603, based on the preliminary direction adjustment angle, the integrated energy consumption balance and control conditions are compared with the preset threshold range to obtain a comparison result.

[0159] Among them, the energy consumption balance and control conditions mean that the adjusted flight energy consumption does not exceed the system's allowable range, and the adjustment angle is within the aircraft's hardware execution capability and dynamic response range.

[0160] For example, in combination with the battery capacity limitation, the calculated preliminary direction adjustment angle is compared with the angle within the aircraft hardware execution capability and dynamic response range to obtain a comparison result.

[0161] In step S604, if the comparison result shows that the direction adjustment angle exceeds the threshold range, a restriction processing mechanism is adopted for the angle, and the angle is clipped through a data screening tool to obtain an adjustment angle range that meets the conditions.

[0162] The restriction processing mechanism sets a reasonable range of values ​​for the angle parameters involved in the system and uses specific algorithms to ensure that these angles remain within a safe, valid, or physically constrained range. Data filtering tools set a valid range for angles and force angle values ​​outside that range to fall within preset upper and lower limits to ensure data conforms to specific rules.

[0163] Exemplarily, the calculated direction adjustment angle exceeds a threshold range, and the upper and lower limits of the angle value need to be adjusted to be within a preset range.

[0164] Step S605: Based on the adjustment angle range that meets the conditions and in combination with the real-time changes in wind direction data and wind speed data, the dynamic impact on the flight direction parameters is analyzed to obtain the optimized flight direction parameters.

[0165] For example, an adjustment angle range that meets the conditions is obtained, and the changes in wind direction data and wind speed data are obtained in real time through a weather station, and the flight direction parameters are adjusted in real time to obtain optimized flight direction parameters.

[0166] In this embodiment, the appropriate direction adjustment angle is obtained through analysis, and the flight direction parameters are adjusted in real time to ensure flight efficiency and safety.

[0167] In one embodiment, the integrating the final flight direction adjustment value and the adjusted path planning data by real-time data integration technology to obtain real-time flight trajectory data of the UAV in a dynamic environment includes:

[0168] In step S701, a unified data structure is generated based on the final flight direction adjustment value and the adjusted path planning data through real-time data integration technology to obtain a standardized flight control data set.

[0169] Among them, standardization refers to converting data of different dimensions, such as angles, coordinates and timestamps, into a unified format that can be recognized by the flight control system.

[0170] For example, the flight direction adjustment value finally obtained and the adjusted path planning data are integrated in real time to unify the spatial coordinates and obtain a standardized real-time flight control data set for real-time execution by the flight system.

[0171] In step S702, a flight instruction is generated based on the standardized flight control data set. The flight instruction is used to instruct the UAV to perform flight according to the flight control data set and to feed back real-time flight trajectory data in a dynamic environment.

[0172] For example, after receiving a standardized flight control data set, the drone parser converts the standardized instructions into control signals that can be recognized by the drone flight control system, performs logistics distribution in order according to priority scheduling, and feeds back real-time flight trajectory data in a dynamic environment.

[0173] In step S703, based on the real-time flight trajectory data, the onboard sensors continuously monitor the changes in obstacle distribution and the updated information of wind direction and speed to obtain monitoring results.

[0174] Among them, airborne sensors can be divided into four categories according to their functions: environmental perception sensors, navigation and positioning sensors, status monitoring sensors and mission payload sensors.

[0175] For example, based on real-time flight trajectory data, the drone's environmental perception sensor continuously monitors the distribution of obstacles and changes in wind direction and speed to obtain updated information.

[0176] In step S704, if a change in obstacle distribution or a wind speed exceeding a preset safety threshold is detected, an adjustment instruction is generated; the adjustment instruction is used to activate the dynamic path adjustment mechanism, adjust the flight trajectory in real time, and obtain updated flight trajectory data of the UAV in a complex environment.

[0177] Among them, the dynamic path adjustment mechanism refers to a complete set of logic and algorithm systems that automatically corrects or replans the original flight path based on real-time perceived environmental changes, its own status or mission requirements when a drone or other intelligent mobile device is performing a mission.

[0178] For example, when it detects a change in obstacle distribution or the wind speed exceeds the preset safety and straight-up limits, the drone automatically activates the path dynamic adjustment mechanism, adjusts the flight trajectory in real time, chooses to avoid obstacles or high wind speed areas, and obtains new flight trajectory data in a complex environment.

[0179] In this embodiment, the flight direction adjustment value and path planning data are converted into a unified format through real-time data integration technology, thereby reducing data processing delays and improving instruction execution efficiency.

[0180] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0181] Based on the same inventive concept, the present application also provides a device for implementing the aforementioned UAV low-altitude logistics delivery path scheduling. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more UAV low-altitude logistics delivery path scheduling device embodiments provided below can be found in the above-mentioned limitations of the UAV low-altitude logistics delivery path scheduling method, and will not be repeated here.

[0182] In an exemplary embodiment, Figure 3 As shown, a UAV low-altitude logistics distribution path scheduling device 300 is provided, including:

[0183] The data acquisition module 301 is used to acquire real-time obstacle distribution data within the delivery area and obtain accurate location information of the obstacle distribution.

[0184] The path planning module 302 is used to perform preliminary calculations on the UAV flight path based on the precise location information to obtain preliminary path planning results.

[0185] The parameter acquisition module 303 is used to obtain path influencing parameters in the preliminary path planning result; the path influencing parameters include wind direction and speed information, resident distribution data and noise-sensitive area information.

[0186] The regional distribution acquisition module 304 is used to obtain the resident distribution area and the noise-sensitive area based on the resident distribution data and the noise-sensitive area information.

[0187] The judgment module 305 is used to judge whether the natural wind speed in the target area in the preliminary path planning result exceeds the preset wind speed threshold of the residential area and the noise-sensitive area, and obtain a judgment result.

[0188] The data updating module 306 is configured to, if the wind speed in the target area exceeds the preset wind speed threshold, eliminate paths with safety scores lower than the preset threshold from the preliminary path planning results to obtain adjusted path segment data.

[0189] The path updating module 307 is configured to update the corresponding path segment in the preliminary path planning result according to the adjusted path segment data to obtain the adjusted path planning data.

[0190] In one embodiment, the data acquisition module 301 is further configured to:

[0191] The original information of building locations and dynamic objects is obtained by scanning, and a preliminary obstacle distribution dataset is obtained.

[0192] Based on the preliminary obstacle distribution data set, a three-dimensional model is constructed to spatially reconstruct the obstacle distribution and obtain three-dimensional structure data containing accurate change information.

[0193] The position change of the dynamic object in the three-dimensional structure data is compared with a preset threshold to obtain a comparison result.

[0194] If the comparison result shows that the position change of the dynamic object exceeds the preset threshold, the dynamic object is marked as a high-priority monitoring object and the real-time movement trajectory of the dynamic object is obtained.

[0195] Based on the real-time moving trajectory, the dynamic changes of obstacle distribution are updated to obtain the latest obstacle distribution data.

[0196] Based on the latest obstacle distribution data, the position of the dynamic object at the preset time point is predicted, and the area where the dynamic object is located at the preset time point is obtained.

[0197] Adjust the scanning frequency according to the area range, increase the data collection density, and obtain accurate location information of obstacle distribution.

[0198] In one embodiment, the data updating module 305 is further configured to:

[0199] If the current wind speed exceeds a preset threshold, a risk marking instruction is generated. The risk marking instruction is used to mark the current area as a high-risk area and obtain a risk marking result.

[0200] Based on the risk marking results, the path segments passing through high-risk areas in the preliminary path planning results are obtained, and the pre-established path optimization model is used for analysis to obtain the set of path segments that need to be adjusted.

[0201] Based on the set of path segments that need to be adjusted, the wind direction information and wind speed information are integrated, and the path segments with safety scores lower than the preset threshold are eliminated to obtain the adjusted path segment data.

[0202] In an exemplary embodiment, a drone low-altitude logistics distribution path scheduling device 400 is provided, comprising:

[0203] The direction adjustment module 401 is used to calculate the angle adjustment value of the flight direction based on the adjusted path planning data through a vector analysis method to obtain a final flight direction adjustment value.

[0204] The data integration module 402 is used to integrate the final flight direction adjustment value and the adjusted path planning data through real-time data integration technology to obtain real-time flight trajectory data of the UAV in a dynamic environment.

[0205] The parameter acquisition module 403 is used to obtain flight time and battery consumption based on the real-time flight trajectory data of the UAV in a dynamic environment.

[0206] The efficiency determination module 404 is used to calculate the actual flight efficiency based on the flight time and battery consumption, determine whether the delivery efficiency requirements are met, and obtain a determination result.

[0207] The path optimization module 405 is used to reduce the flight distance according to the waypoint optimization formula to obtain an optimized flight path if the judgment result is that the delivery efficiency requirement is not met.

[0208] The heading angle adjustment module 406 is configured to adjust the heading angle based on the optimized flight path and adopt the principle of maximizing ground speed to obtain final adjusted flight planning data.

[0209] In one embodiment, the direction adjustment module 401 is further configured to:

[0210] Based on the adjusted path planning data, the vector analysis method is used to calculate the angle between the wind direction and the flight direction to obtain the optimized flight direction parameters.

[0211] Based on the optimized flight direction parameters, combined with the obtained resident distribution data and noise-sensitive area information, it is determined whether the optimized flight direction parameters cause the noise impact to exceed the preset noise threshold, and a judgment result is obtained.

[0212] If the judgment result is that the noise impact exceeds the preset noise threshold, the flight direction is changed, the flight altitude is increased, or the flight attitude is adjusted to obtain the final flight direction adjustment value.

[0213] In one embodiment, the direction adjustment module 401 is further configured to:

[0214] From the adjusted path planning data, the flight direction angle and wind direction angle of the flight direction are obtained.

[0215] Based on the flight direction angle and wind direction angle of the flight direction, the vector analysis method is used to calculate the angle between the wind direction and the current flight direction to obtain the preliminary direction adjustment angle:

[0216] α=|(φ-θ)|

[0217]

[0218] Where θ is the flight direction angle, φ is the wind direction angle, α is the angle between the wind direction and the current flight direction, β is the initial direction adjustment angle, W is the wind speed, and V is the aircraft airspeed.

[0219] Based on the preliminary direction adjustment angle, the fusion energy consumption balance and control conditions are compared with the preset threshold range to obtain the comparison results.

[0220] If the comparison result shows that the direction adjustment angle exceeds the threshold range, a restriction processing mechanism is adopted for the angle, and the angle is clipped through a data screening tool to obtain an adjustment angle range that meets the conditions.

[0221] Based on the adjustment angle range that meets the conditions and combined with the real-time changes of wind direction data and wind speed data, the dynamic impact on the flight direction parameters is analyzed to obtain the optimized flight direction parameters.

[0222] In one embodiment, the data integration module 402 is further configured to:

[0223] According to the final flight direction adjustment value and the adjusted path planning data, a unified data structure is generated through real-time data integration technology to obtain a standardized flight control data set.

[0224] Based on the standardized flight control data set, flight instructions are generated; flight instructions are used to instruct the UAV to perform flight according to the flight control data set and feedback real-time flight trajectory data in a dynamic environment.

[0225] Based on real-time flight trajectory data, the onboard sensors continuously monitor the changes in obstacle distribution and updated information on wind direction and speed to obtain monitoring results.

[0226] If changes in obstacle distribution are detected or wind speed exceeds the preset safety threshold, an adjustment instruction is generated; the adjustment instruction is used to activate the dynamic path adjustment mechanism, adjust the flight trajectory in real time, and obtain updated flight trajectory data of the UAV in complex environments.

[0227] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the drone low-altitude logistics distribution path scheduling method as described above are implemented.

[0228] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0229] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to the partial description of the method embodiments. The device embodiments described above are merely illustrative, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0230] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. A method for scheduling low-altitude logistics distribution routes using drones, characterized in that: The method comprises: Obtain real-time obstacle distribution data within the delivery area and obtain accurate location information of obstacle distribution; Based on the precise location information, a preliminary calculation is performed on the flight path of the UAV to obtain a preliminary path planning result; Obtaining path influencing parameters from the preliminary path planning result; the path influencing parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information; Based on the resident distribution data and the noise-sensitive area information, obtaining resident distribution areas and noise-sensitive areas; Determining whether the natural wind speed in the target area in the preliminary path planning result exceeds the preset wind speed thresholds of the residential area and the noise-sensitive area, and obtaining a determination result; If the judgment result is that the natural wind speed in the target area exceeds the preset wind speed threshold, the paths with safety scores lower than the preset threshold are eliminated from the preliminary path planning results to obtain adjusted path segment data; According to the adjusted path segment data, the corresponding path segment in the preliminary path planning result is updated to obtain the adjusted path planning data.

2. The method according to claim 1, characterized in that Obtaining the obstacle distribution data on the delivery path to obtain accurate location information of the obstacle distribution includes: The original information of building locations and dynamic objects is obtained by scanning to obtain a preliminary obstacle distribution dataset; Based on the preliminary obstacle distribution data set, a three-dimensional model is constructed to spatially reconstruct the obstacle distribution to obtain three-dimensional structure data containing accurate change information; Comparing the position change of the dynamic object in the three-dimensional structure data with a preset threshold value to obtain a comparison result; If the comparison result shows that the position change of the dynamic object exceeds the preset threshold, the dynamic object is marked as a high-priority monitoring object, and the real-time movement trajectory of the dynamic object is obtained; Based on the real-time moving trajectory, the dynamic changes of obstacle distribution are updated to obtain the latest obstacle distribution data; Predicting the position of the dynamic object at a preset time point based on the latest obstacle distribution data to obtain the area where the dynamic object is located at the preset time point; The scanning frequency is adjusted according to the area range, the data collection density is increased, and the accurate location information of the obstacle distribution is obtained.

3. The method according to claim 1, characterized in that If the judgment result is that the wind speed in the target area exceeds the preset wind speed threshold, the paths with safety scores lower than the preset threshold are eliminated from the preliminary path planning results to obtain adjusted path segment data, including: If the wind speed in the target area exceeds a preset wind speed threshold, a risk marking instruction is generated, wherein the risk marking instruction is used to mark the target area as a high-risk area, and a risk marking result is obtained; According to the risk marking result, the path segments passing through the high-risk area in the preliminary path planning result are obtained, and a pre-established path optimization model is used to analyze them to obtain a set of path segments that need to be adjusted; Based on the set of path segments that need to be adjusted, wind direction information and wind speed information are integrated, and path segments with safety scores lower than a preset threshold are eliminated to obtain adjusted path segment data.

4. The method according to claim 1, wherein The method further comprises: Based on the adjusted path planning data, calculating the angle adjustment value of the flight direction by a vector analysis method to obtain a final flight direction adjustment value; Integrating the final flight direction adjustment value and the adjusted path planning data through real-time data integration technology to obtain real-time flight trajectory data of the UAV in a dynamic environment; Obtaining flight time and battery consumption based on real-time flight trajectory data of the drone in a dynamic environment; Calculate the actual flight efficiency based on the flight time and battery consumption, determine whether the delivery efficiency requirement is met, and obtain a determination result; If the judgment result is that the delivery efficiency requirement is not met, the flight distance is reduced according to the waypoint optimization formula to obtain an optimized flight path; Based on the optimized flight path, the heading angle is adjusted using the ground speed maximization principle to obtain final adjusted flight planning data.

5. The method according to claim 4, characterized in that The step of calculating the angle adjustment value of the flight direction based on the adjusted path planning data by a vector analysis method to obtain a final flight direction adjustment value includes: Based on the adjusted path planning data, a vector analysis method is used to calculate the angle between the wind direction and the flight direction to obtain optimized flight direction parameters; Based on the optimized flight direction parameters, combined with the acquired resident distribution data and the noise-sensitive area information, determining whether the optimized flight direction parameters cause the noise impact to exceed a preset noise threshold, and obtaining a determination result; If the judgment result is that the noise impact exceeds the preset noise threshold, the flight direction is changed, the flight altitude is increased, or the flight attitude is adjusted to obtain a final flight direction adjustment value.

6. The method according to claim 5, characterized in that The method of calculating the angle between the wind direction and the flight direction based on the adjusted path planning data to obtain optimized flight direction parameters includes: Obtaining a flight direction angle and a wind direction angle of the flight direction from the adjusted path planning data; Based on the flight direction angle and wind direction angle of the flight direction, a vector analysis method is used to calculate the angle between the wind direction and the current flight direction to obtain a preliminary direction adjustment angle: α=|(φ-θ)| Where θ is the flight direction angle, φ is the wind direction angle, α is the angle between the wind direction and the current flight direction, β is the initial direction adjustment angle, W is the wind speed, and V is the aircraft airspeed; Based on the preliminary direction adjustment angle, the fusion energy consumption balance and control conditions are compared with a preset threshold range to obtain a comparison result; If the comparison result shows that the direction adjustment angle exceeds the threshold range, a restriction processing mechanism is adopted for the angle, and the angle is clipped through a data screening tool to obtain an adjustment angle range that meets the conditions; Based on the adjustment angle range that meets the conditions, combined with the real-time changes in wind direction data and wind speed data, the dynamic impact on the flight direction parameters is analyzed to obtain the optimized flight direction parameters.

7. The method according to claim 4, characterized in that The real-time data integration technology is used to integrate the final flight direction adjustment value and the adjusted path planning data to obtain real-time flight trajectory data of the UAV in a dynamic environment, including: generating a unified data structure based on the final flight direction adjustment value and the adjusted path planning data through real-time data integration technology to obtain a standardized flight control data set; Generate flight instructions based on the standardized flight control data set, wherein the flight instructions are used to instruct the UAV to perform flight according to the flight control data set and feedback real-time flight trajectory data in a dynamic environment; Based on the real-time flight trajectory data, continuously monitoring the obstacle distribution changes and the updated information of the wind direction and speed through onboard sensors to obtain monitoring results; If the obstacle distribution changes or the wind speed exceeds the preset safety threshold range, an adjustment instruction is generated; the adjustment instruction is used to activate the path dynamic adjustment mechanism, adjust the flight trajectory in real time, and obtain updated flight trajectory data of the UAV in a complex environment.

8. UAV low-altitude logistics distribution path scheduling device, characterized by: The device comprises: The data acquisition module is used to obtain real-time obstacle distribution data within the delivery area and obtain accurate location information of the obstacle distribution; A path planning module is used to perform preliminary calculations on the flight path of the UAV based on the precise location information to obtain a preliminary path planning result; A parameter acquisition module is used to obtain path influencing parameters from the preliminary path planning result; the path influencing parameters include wind direction and speed information, resident distribution data, and noise-sensitive area information; an area distribution acquisition module, configured to obtain a resident distribution area and a noise-sensitive area based on the resident distribution data and the noise-sensitive area information; a judgment module, configured to judge whether the natural wind speed in the target area in the preliminary path planning result exceeds the preset wind speed thresholds of the residential area and the noise-sensitive area, and obtain a judgment result; a data updating module configured to, if the judgment result is that the wind speed in the target area exceeds the preset wind speed threshold, eliminate paths with safety scores lower than the preset threshold in the preliminary path planning results, and obtain adjusted path segment data; The path updating module is used to update the corresponding path segment in the preliminary path planning result according to the adjusted path segment data to obtain the adjusted path planning data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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